serge-ss-paille

5 ms·

1. 1.▲
   Feedback on Using Unified Builds(serge-sans-paille.github.io)

   1 points by serge-ss-paille 1mo ago|0 comments
2. 2.▲
   A Hidden Weakness(serge-sans-paille.github.io)

   35 points by serge-ss-paille 1y ago|1 comments
3. 3.▲
   Bit twiddling hack to quickly create a bitset from an array of boolean(github.com)

   3 points by serge-ss-paille 2y ago|0 comments
4. 4.▲
   Experiments with Byte Matrix Multiplication(github.com)

   41 points by serge-ss-paille 2y ago|4 comments
5. 5.▲
   Reducing the ecological impact of computing through education and Python comp(nature.com)

   1 points by serge-ss-paille 5y ago|0 comments
6. 6.▲
   Pythran 0.9.7 released and compared to numba and stock Python(serge-sans-paille.github.io)

   3 points by serge-ss-paille 6y ago|0 comments
7. 7.▲
   Basic value range analysis for Pythran programs(serge-sans-paille.github.io)

   1 points by serge-ss-paille 6y ago|0 comments
8. 8.▲
   by serge-ss-paille 7y ago

   More materials for the curious: Some benchmarks here: http://serge-sans-paille.github.io/pythran-stories/testing-p... Some more benchmark you can run on you own: https://github.com/serge-sans-paille&#x2
9. 9.▲
   Pythran – a compiler for Python scientific kernels – release(serge-sans-paille.github.io)

   60 points by serge-ss-paille 7y ago|6 comments
10. 10.▲
    Pythran Compiler Release(freelists.org)

    2 points by serge-ss-paille 7y ago|0 comments
11. 11.▲
    Static Analysis of Python Programs with Beniget(serge-sans-paille.github.io)

    2 points by serge-ss-paille 8y ago|0 comments
12. 12.▲
    Pythran Case Study: Simulated Annealing Applied to the TSP(medium.com)

    1 points by serge-ss-paille 8y ago|0 comments
13. 13.▲
    New Release of the Pythran Compiler for Numeric Python(serge-sans-paille.github.io)

    1 points by serge-ss-paille 8y ago|0 comments
14. 14.▲
    by serge-ss-paille 8y ago

    > could you tell us the shortcomings of Cython In order to achieve top performance, in the context of numerical simulations, you generally end up explicity writing the loops are implicit in high-level numpy (less abstraction). Cython doe
15. 15.▲
    Pythran as a bridge between fast prototyping and code deployment(serge-sans-paille.github.io)

    108 points by serge-ss-paille 8y ago|25 comments
16. 16.▲
    What is the air speed velocity of Pythran?(serge-sans-paille.github.io)

    1 points by serge-ss-paille 8y ago|0 comments
17. 17.▲
    Testing Pythran on random kernels(serge-sans-paille.github.io)

    1 points by serge-ss-paille 8y ago|0 comments
18. 18.▲
    An Incursion into Basic ML – Gradient Descent Compiled with Pythran(serge-sans-paille.github.io)

    1 points by serge-ss-paille 8y ago|0 comments
19. 19.▲
    by serge-ss-paille 8y ago

    (shameful author here) One should read def rosen_explicit_loop(x): s = 0. n = x.shape[0] for i in range(0, n - 1): s += 100. * (x[i + 1] - x[i] ** 2.) ** 2. + (1 - x[i]) ** 2 return s (edited)
20. 20.▲
    by serge-ss-paille 8y ago

    (main dev writting) Don't focus too much on the claimless :-) There still are far more wide spread tools that solve the same kind of issues (numba, cython, julia)... The idea of the change was that it's more important to convey
21. 21.▲
    Pythran: Crossing the Python Frontier [pdf](computer.org)

    122 points by serge-ss-paille 8y ago|68 comments
22. 22.▲
    Frozen – An header-only, constexpr alternative to gperf for C++14 users(blog.quarkslab.com)

    1 points by serge-ss-paille 9y ago|0 comments
23. 23.▲
    Unsound type checking for a Python subset(serge-sans-paille.github.io)

    1 points by serge-ss-paille 10y ago|0 comments
24. 24.▲
    Playing with compiler flags for size and perf(serge-sans-paille.github.io)

    2 points by serge-ss-paille 11y ago|0 comments